摘要
为了提高手写体数字的识别率,在AlexNet网络模型的基础上进行改进,引入Inception-resnet模块替换模型中的Conv3和Conv4来提升模型的特征提取能力;使用批归一化处理(BN)方法加快网络的收敛速度,防止过拟合;减少卷积核的数量,提升网络的训练速度。在MNIST数据集上进行训练与测试,实验结果表明改进的网络模型具有较高的检测精度,达到了0.9966,证明了本算法的有效性。
In order to improve the recognition rate of handwritten numbers,we have improved AlexNet network model in this paper.Conv3 and Conv4 were introduced to replace the model for Inception-resnet module,which improves the feature extraction capability of the model.The Batch Normalization(BN)method was used to accelerate network convergence and prevent overfitting,reducing the number of convolutional kernels and improving the training speed of the network.In this paper,training and testing are carried out on MNIST data sets.Experimental results show that the improved network model has a better detection accuracy of 0.9966,which proves the effectiveness of the algorithm.
作者
谢东阳
李丽宏
苗长胜
XIE Dongyang;LI Lihong;MIAO Changsheng(School of Information and Electrical Engineering,Hebei University of Engineering,Handan,Hebei 056038,China)
出处
《河北工程大学学报（自然科学版）》
CAS
2021年第4期102-106,共5页
Journal of Hebei University of Engineering:Natural Science Edition
基金
河北省省级科技计划资助项目(20475702D)
邯郸市科学技术局项目(19422031008-14)。
